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Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark.

Cong Zhang, Hongbo Bi, Tian-Zhu Xiang

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    |October 27, 2023
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    This study introduces collaborative camouflaged object detection (CoCOD) and the CoCOD8K dataset. The proposed BBNet model effectively detects camouflaged objects by exploring inter-image and intra-image cues.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Camouflaged object detection (COD) is challenging due to objects blending with backgrounds.
    • Existing methods often focus on single images, lacking collaborative context.
    • A large-scale dataset for collaborative camouflaged object detection is needed.

    Purpose of the Study:

    • Introduce a new task: collaborative camouflaged object detection (CoCOD).
    • Present the first large-scale dataset (CoCOD8K) for CoCOD.
    • Propose a novel baseline model (BBNet) for CoCOD.

    Main Methods:

    • Constructed CoCOD8K dataset with 8528 images across diverse camouflage scenarios.
    • Developed BBNet with inter-image collaborative feature exploration (CFE) and intra-image object feature search (OFS) modules.
    • Integrated a local-global refinement (LGR) module for enhanced detection.

    Main Results:

    • Benchmarked 18 state-of-the-art models on the CoCOD8K dataset.
    • BBNet demonstrated significantly superior performance compared to existing COD and CoSOD algorithms.
    • Extensive experiments validated the effectiveness of the proposed BBNet model.

    Conclusions:

    • The CoCOD8K dataset and BBNet model provide a strong foundation for CoCOD research.
    • The proposed approach effectively leverages collaborative information for improved camouflaged object detection.
    • This work aims to advance the field of camouflaged object detection.